Magnetic Resonance Compatible Tactile Force Sensing Using Optical Fibres for Minimally Invasive Surgery

Abstract

This thesis presents research in design, fabrication and testing of magnetic resonance (MR) compatible tactile array sensors based on light intensity modulation using opti-cal fibres. The popularity of minimally invasive surgery (MIS) opens the field of tac-tile sensing for medical use, especially in MR environment. The departure from con-ventional sensing approaches (such as capacitive and piezoresistive) allows the devel-opment of tactile sensors which are low cost, small in size, lightweight, free from electromagnetic interference, water and corrosion resistant and capable to operate in harsh environments. In the framework of this PhD study, a number of MR compatible tactile array sensors have been developed, including uniaxial tactile array sensors and an x- and y-axis lateral contact sensor. Mathematical models for these newly-devel-oped tactile sensors have been created and verified. Force is measured through the displacement of a flexible structure with a known stiffness, modulating in turn the light intensity in the employed optical fibres. For the tactile array sensor, a 2D vision system is applied to detect light signals from all sensing elements via the optical fibres – this new approach provides a great potential for high density tactile array sensing, employing a low-cost vision sensor. For the lateral sensor, high-speed/high-sensitivity detectors are utilized to calculate contact force position and magnitude. Combined with 3D printing technology, a miniature tactile probe head capable of palpation in MIS has been designed and tested in ex vivo tissue palpation experiments. All sensor systems developed in this thesis are MR compatible and immune to electromagnetic noise. The proposed sensing structures and principles show high miniaturization and resolution capabilities, making them suitable for integration with medical tools.

Date of Award	2015
Original language	English
Awarding Institution	King's College London
Supervisor	Hongbin Liu (Supervisor) & Kaspar Althoefer (Supervisor)